SOTAVerified

Depth Estimation

Depth Estimation is the task of measuring the distance of each pixel relative to the camera. Depth is extracted from either monocular (single) or stereo (multiple views of a scene) images. Traditional methods use multi-view geometry to find the relationship between the images. Newer methods can directly estimate depth by minimizing the regression loss, or by learning to generate a novel view from a sequence. The most popular benchmarks are KITTI and NYUv2. Models are typically evaluated according to a RMS metric.

Source: DIODE: A Dense Indoor and Outdoor DEpth Dataset

Papers

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Showing 1751–1800 of 2454 papers

TitleStatusHype
Deterministic Guided LiDAR Depth Map Completion—0
DEUX: Active Exploration for Learning Unsupervised Depth Perception—0
DFineNet: Ego-Motion Estimation and Depth Refinement from Sparse, Noisy Depth Input with RGB Guidance—0
DGNR: Density-Guided Neural Point Rendering of Large Driving Scenes—0
DH-VTON: Deep Text-Driven Virtual Try-On via Hybrid Attention Learning—0
DiffPoseNet: Direct Differentiable Camera Pose Estimation—0
Diffusion-Augmented Depth Prediction with Sparse Annotations—0
Diffusion-based Light Field Synthesis—0
Diffusion Handles: Enabling 3D Edits for Diffusion Models by Lifting Activations to 3D—0
Diffusion Handles Enabling 3D Edits for Diffusion Models by Lifting Activations to 3D—0
Diffusion Methods for Classification with Pairwise Relationships—0
DiffuStereo: High Quality Human Reconstruction via Diffusion-based Stereo Using Sparse Cameras—0
DiFuse-Net: RGB and Dual-Pixel Depth Estimation using Window Bi-directional Parallax Attention and Cross-modal Transfer Learning—0
Dilated Fully Convolutional Neural Network for Depth Estimation from a Single Image—0
Dimensions of Motion: Monocular Prediction through Flow Subspaces—0
DINO-SD: Champion Solution for ICRA 2024 RoboDepth Challenge—0
Direct, Dense, and Deformable: Template-Based Non-Rigid 3D Reconstruction From RGB Video—0
Direct Depth Learning Network for Stereo Matching—0
Direct Handheld Burst Imaging to Simulated Defocus—0
Direction Matters: Depth Estimation With a Surface Normal Classifier—0
Disambiguating Monocular Depth Estimation with a Single Transient—0
Discovering Multiple Algorithm Configurations—0
Discrete-Continuous Depth Estimation from a Single Image—0
Discriminative Training of Deep Fully-connected Continuous CRF with Task-specific Loss—0
DISeR: Designing Imaging Systems with Reinforcement Learning—0
DisNet: A novel method for distance estimation from monocular camera—0
Disparity-Aware Domain Adaptation in Stereo Image Restoration—0
Distilling Monocular Foundation Model for Fine-grained Depth Completion—0
Distill Knowledge from NRSfM for Weakly Supervised 3D Pose Learning—0
DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features—0
Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images—0
Distortion-aware Monocular Depth Estimation for Omnidirectional Images—0
Distortion-Aware Self-Supervised 360° Depth Estimation from A Single Equirectangular Projection Image—0
Distortion-Tolerant Monocular Depth Estimation On Omnidirectional Images Using Dual-cubemap—0
Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection—0
DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes—0
D-NPC: Dynamic Neural Point Clouds for Non-Rigid View Synthesis from Monocular Video—0
DO3D: Self-supervised Learning of Decomposed Object-aware 3D Motion and Depth from Monocular Videos—0
DOC-Depth: A novel approach for dense depth ground truth generation—0
Does depth estimation help object detection?—0
Domain Adaptive Monocular Depth Estimation With Semantic Information—0
Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation—0
Domain-Transferred Synthetic Data Generation for Improving Monocular Depth Estimation—0
Don't Forget The Past: Recurrent Depth Estimation from Monocular Video—0
Double Refinement Network for Efficient Indoor Monocular Depth Estimation—0
DoubleStar: Long-Range Attack Towards Depth Estimation based Obstacle Avoidance in Autonomous Systems—0
DoubleTake: Geometry Guided Depth Estimation—0
DPBridge: Latent Diffusion Bridge for Dense Prediction—0
DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion—0
DPS-Net: Deep Polarimetric Stereo Depth Estimation—0
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All datasetsStanford2D3D PanoramicNYU-Depth V2DCMeBDthequeScanNetV2Cityscapes testDIODEKITTI 2015Mars DTM EstimationScanNet4D Light Field DatasetKITTI Eigen split

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OmniDepthRMSE0.62—Unverified
2SphereDepthRMSE0.45—Unverified
3Jin et al.RMSE0.42—Unverified
4BiFuse with fusionRMSE0.41—Unverified
5HoHoNet (ResNet-101)RMSE0.38—Unverified
6PanoDepthRMSE0.37—Unverified
7BiFuse++RMSE0.37—Unverified
8UniFuse with fusionRMSE0.37—Unverified
9DisConvRMSE0.37—Unverified
10SliceNetRMSE0.37—Unverified
#ModelMetricClaimedVerifiedStatus
1A2JmAP8.61—Unverified
2PAD-NetRMS0.79—Unverified
3MS-CRFRMS0.59—Unverified
4DORNRMS0.51—Unverified
5FreeformRMS0.43—Unverified
6Optimized, freeformRMS0.43—Unverified
7VNLRMS0.42—Unverified
8BTSRMS0.41—Unverified
9TransDepth (AGD+ ViT)RMS0.37—Unverified
10AdaBinsRMS0.36—Unverified
#ModelMetricClaimedVerifiedStatus
1T2NetAbs Rel0.35—Unverified
2MIDASAbs Rel0.31—Unverified
3Bhattacharjee et al.Abs Rel0.25—Unverified
#ModelMetricClaimedVerifiedStatus
1T2NetAbs Rel0.49—Unverified
2MIDASAbs Rel0.42—Unverified
3Bhattacharjee et al.Abs Rel0.38—Unverified
#ModelMetricClaimedVerifiedStatus
1LeReSabsolute relative error0.1—Unverified
2DELTASabsolute relative error0.09—Unverified
3Distill Any Depthabsolute relative error0.04—Unverified
#ModelMetricClaimedVerifiedStatus
1SDC-DepthRMSE6.92—Unverified
2SwinMTLRMSE6.35—Unverified
#ModelMetricClaimedVerifiedStatus
1AIP-BrownDelta < 1.250.36—Unverified
2LeResDelta < 1.250.23—Unverified
#ModelMetricClaimedVerifiedStatus
1H-Net (Ours)Absolute relative error (AbsRel)0.09—Unverified
2H-Net (Ours) Full EigenAbsolute relative error (AbsRel)0.08—Unverified
#ModelMetricClaimedVerifiedStatus
1GLPDepthDelta < 1.250.43—Unverified
2SRDINET (Model A)Delta < 1.250.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Atlas (finetuned)RMSE0.17—Unverified
2Atlas (plain)RMSE0.17—Unverified
#ModelMetricClaimedVerifiedStatus
1LFattNetBadPix(0.01)17.23—Unverified
#ModelMetricClaimedVerifiedStatus
1LightDepthNumber of parameters (M)42.6—Unverified
#ModelMetricClaimedVerifiedStatus
1UniFuseAbs Rel0.11—Unverified
#ModelMetricClaimedVerifiedStatus
1X-TC (Cross-Task Consistency)L1 error1.63—Unverified